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Nerval's Lobster writes "Can data-analytics software win a Super Bowl? That's what the Buffalo Bills are betting on: the NFL team will create an analytics department to crunch player data, building on a model already well established in professional baseball and basketball. 'We are going to create and establish a very robust football analytics operation that we layer into our entire operation moving forward,' Buffalo Bills president Russ Brandon recently told The Buffalo News. 'That's something that's very important to me and the future of the franchise.' The increased use of analytics in other sports, he added, led him to make the decision: 'We've seen it in the NBA. We've seen it more in baseball. It's starting to spruce its head a little bit in football, and I feel we're missing the target if we don't invest in that area of our operation, and we will.'"

I for one hope the new Browns owner decides to go down a similar path, for a decade the Browns have squandered draft choices and money on flop after flop. Since they're in the market for a new GM and head coach now would be the perfect time to inject such a new system into the front office.

Hey, at least you guys have actually made it to the superbowl and you're in a much smaller media market with arguably a less fanatical fanbase (it's taken almost two decades of mismanagement for any significant percentage of seats to go unsold)

It's catching on a bit later in sports other than baseball due to the difficulty (until recently) of collecting fine-grained statistics in many sports. Baseball is fairly discrete: it operates pitch by pitch, with a lot of down time in between. So a large number of relevant statistics can be tallied by hand, which is why we have piles of statistics dating back decades. For every pitch, you can mark down whether it was a strike or ball, whether the batter swung, where in the field it went to if hit, what the fielder did with it, what the runners did in response, etc.

For football (and even more so, soccer), a lot of the relevant information you'd get from watching replays is more "continuous" and harder to extract manually. Traditional statistics did measured things like passing completion percentage and yards gained by a running back, but they didn't collect data that could be used to quantify things like the quality of an offensive line, or of blockers, except indirectly through overall team performance. Now a lot more of that information is being automatically tallied using computer-vision algorithms churning through digitized camera footage.

Doesn't sound like the funnest of jobs, but as a beginning maybe they can hire somebody to watch play by play after the game and do some data entry. The algorithms can always be built up later. Statistics like this are cool, they help teams fill gaps with weaknesses that may not be apparent watching the team everyday and help to enrich the sport, but they're not to be confused with talent and smarts.

Not only this, but it can be difficult to determine whether something like a drop or an interception was the fault of the quarterback or the wide receiver. Drops aren't an official statistic for this reason.

That's where statistics become useful! If a quarterback's throws are consistently dropped by a single receiver then it's the receiver's fault. This can be missed when looking at each play on an individual basis, but in aggregate over a season or two could reveal a significant weakness in the receiving corps or the quarterback.

But these are the kind of statistics they already have, and can already very easily analyze. The problem is that there just aren't enough data points. A good receiver may get 10 targets per game, and a quarterback is going to through on average 1 interception per game out of 40 attempts. That means an average receiver will probably have 4 passes intended for them intercepted each year. Lets say that half of interceptions are the receiver's fault, so that makes 2 per year. You simply are not going to ge

Too often the quarterback takes a bum rap for butter fingers down field, just like pitchers gain poor ERAs due to bad defense that allows easily defended ground balls to become runs scored, or pitchers that have bad win/loss records while playing with a horrible bunch of hitters.

Entire new measurements must be defined for football. How do you measure if the pass was catch-able by a competent receiver? Is it anything within arms length? Or is it more complex, taking into account direction of player motion? What about defensive coverage? Every one of these has to be assigned some form of measurement and then you have to start digitizing game tapes. It will take years to develop anything approximating what baseball has, but its probably long past due.

, just like pitchers gain poor ERAs due to bad defense that allows easily defended ground balls to become runs scored

Just an FYI... ERA is Earned Run Average. It does not include runs arising from errors made by fielders (those are unearned runs).

Of course, there is still a very human factor in determining what defensive mishap is scored an error instead of a hit... not to mention great defenders who turn what would be a hit into an out because of their athletic range.

just like pitchers gain poor ERAs due to bad defense that allows easily defended ground balls to become runs scored

By definition, and "easily defended ground ball" that does not result in an out is an error, and thus the runs that result from it are likely unearned. Since "ERA" is the earned run average, that pitcher's statistic generally would not be hurt by such plays.

And, sabremetrics has plenty of stats that deal with such situations, like component ERA, defense-independent ERA, and even ERA+ (which adjusts for the ballpark).

The fine grained statistics that you can pull out of baseball go back over a hundred years. Every game has these gathered more or less automatically these days, and even from a box score one can piece together stats on each player's abilities.

As a result there are plays that are never even attempted anymore because the stats are so precise at describing the likelihood of success/failure that they virtually dictate how the game is played. A player's value at any given point in time can be measured ver

This level of detail is missing from Football, in part simply because too many bodies are in motion at once making it hard and tedious to map them, evaluate them, describe them, measure them, etc.

Yup, although the same issue applies to basketball. Baseball is fairly unique in that any given play usually is only influenced by 3-4 people. The pitcher is in every play, the batter is in every play, and then there might be a fielder where the ball goes, and maybe one or two where the ball is tossed, in particular first base). Whether the batter makes it to first is probably 95% of the entire game, thus the concept of batting average.

It’s not the down time, it is the discrete objective action vs. continuous subjective actions.

For baseball you have 1 pitcher and 1 man on bat. The batter can either strike out or the ball could go someplace – left field, right field, etc. You can then measure the result – did he get on base – did he advance any runners, etc. One can break things down into actions.

Other games are harder. In football there is much more interaction with you teammates - didn’t George Carlin say

Hint: Baseball can, for the most part, be broken down into a model of a face off between the pitcher and the batter.

Football is MUCH more of a team sport. It's far more difficult to tease out whether that running back is good because he's good, or whether he's good because his offensive line is leveling the defensive tackles and linebackers.

Check out places like Football Outsiders, which have tons of fantastic statistics for College and Pro teams, but individual player stats are... lacking.

Their success will likely depend on how much effort they put into collecting data. If all they look at is the same statistics you can find at CBS Sports, Football Outsiders, etc. then it will probably not help at all. But if they really get serious about data collection, who knows how much insight they could gain.

There are about 130 plays per game, and 256 games per year. That is 33,280 plays to analyze each year. That would increase to about 135k if you include Division 1-A college games. If you had two guys spend 15 minutes analyzing each play (2 guys to reduce errors) then it would take 20 full time employees to do this each year. More if you want to get more immediate results after each week. There are plenty of ex-athletes that couldn't make the pros and are intelligent enough for the work. Probably somewhere around $2 million per year in salary ($500k if you only look at professional games).

Just think of all the information you could gain. The first team to get this right could probably greatly improve their overall defenses and their offensive lines (positions that are very hard to rate with stats). I wonder how many teams know how many seconds thier offensive tackles can block an average defensive lineman, adjusted for their quarterback's mobility on each play, and any number of other mitigating factors.

Computerized game tape analysis should shorten the time required by quite a lot.HD tv cams are becoming so cheap and so good, that teams could put time-synced cameras in two (high) mounts for each game. The league could assure that both teams have access to the video to do their own analysis, or maybe just the digitized results. I don't think it takes 20 employees to do this.

There are about 130 plays per game, and 256 games per year. That is 33,280 plays to analyze each year. That would increase to about 135k if you include Division 1-A college games. If you had two guys spend 15 minutes analyzing each play (2 guys to reduce errors) then it would take 20 full time employees to do this each year. More if you want to get more immediate results after each week. There are plenty of ex-athletes that couldn't make the pros and are intelligent enough for the work. Probably somewhere a

It may take longer than 15 minutes to analyze a play, but I doubt it is too much more. At 22 minutes, you can spend a full minute analyzing each player. And some plays only include half of the players on the field. It would take much more than 22 minutes to get every possible statistic about a play, but you could at least get some useful metrics about each players' performance in just a minute.

So say you just look at the handful of players directly involved in a play... say, ten of them. Do you just look at the yards gained? How do you account for things like down and distance factoring into the style of play? A 2-yard run on 3rd-and-1 is much more valuable than a 4-yard run on 2nd-and-10... so how do your statistics weight that?

That is the easy part. Football Outsiders already does that. There are some fairly basic ways to determine a s

In baseball, you don't measure how well a player bunts or takes a few pitches or leads off base for a steal... how does not matter - it is only the outcome.

Same for football - don't care what a QB does pre-snap or how he receives the snap, or how a WR runs a route...

instead it is a simple question - does the team score more points with or without the player on the field and typically this comes down to whether the team has more yards (or opposition less yards for defensive assessment)

Of all the comments I've read so far, yours is the closest to what I would consider the case. Most commenters seem to be taking the same discrete statistical analysis approach that has been common for many years, and point out correctly that it's too difficult to follow and 'score' all those players' actions. But they are ignoring the progress of the last 20 years in probabilistic statistics such as Bayesian analysis (as opposed to 'frequentist' statistics, the traditional form), and machine learning meth

You should read Soccernomics. A lot of the principles that Sabermetics has applied to soccer as well which is as a team sport as well. Specifically teams like Lyon and FC Porto have been doing something similar and exceeding expectations for many years. Manchester City who also have a ton of money, have now published their players' stats [mcfc.co.uk].

Ok, we've input the performance data of both teams, hit the big analyze button, and waited weeks for the answer to how the Bills can win the Super Bowl. It's formulated an answer and... and... AND... Error 404, Universe not found? What does that mean?

Ok, I can see it for baseball. There is close to no interplay between players (even on the pitching team, coordination is restricted to whether you can catch what someone throws at you), and strategy is restricted to positioning players where a batter tends to hit and to how aggressively you go after a pitcher or batter. You're also playing 162 games a year - you can get some pretty good numbers in that time. Basketball is a bit harder, but with only four other teammates on the floor and a fairly static match-up (guards don't face centers much, you have zone or man-defense, and strategy revolves around how much you go for inside battles versus outside shots), the possible factors that influence whether a shot is made or not is still pretty small. You're also playing 82 games and taking a significant number of shots in a game. Again, you have a decent data set to work with.

But football? There are 10 teammates on the field, quite a few of which get switched out every other snap. You have 52 people on the roster, with many of them active during every game (especially on defense). Strategic decisions can take specific players completely out of the game for long stretches (simplest example: you're behind in the game, and start throwing - does that mean your running backs now suck?). And finally: there's only 16 games in a season. Some people may see action only 2-3 times a game or see action in trivial circumstances (see: kicker, long snapper). So not only do you have a huge amount of variables influencing a single player's success, you will also have a hard time creating a metric for success (touchdowns and sacks are rare outcomes of a long string of events), and on top of that, you're frequently dealing with a data set that maybe consists of 100 data points for an entire year, and maybe of 10 points for some lower-rung players. And it's exactly in the lower rungs of the players where moneyball was so wildly successful. Everybody knows an Adrian Peterson and Derek Jeter when they see one, but what about the journey players who switch teams once a year? Moneyball pretty much addressed that problem in baseball, but I don't see it working in football.

The Bills might prove me wrong, but I see this instead turning into the problem Girardi had with the Yankees: making player decisions based on stats that are calculated with 5 data points leads to decisions that will come back to bite you in the long run. You might as well save the money and just flip a coin.

Key is don't focus on score - that works for baseball and basketball but not for football.

Make the assumption that "yards = wins" and each play is a discrete event with yardage outcome - suddenly you have 1000s of events per year.

And the switching out of players may actually be the key to evaluating performance of the bulk of the players ie the offensive and defense lines... It becomes much easier to determine offensive yardage with Player X on the field is "0.1 yard per play" better than with Player Y on

You can't make a general utility statistic like VORP for most football positions, but you can measure a lot of discrete situations. Then you use old-fashioned football knowledge to assess how that measurement is relevant to the position and the function of the team as a whole.

You're still going to have to make a lot more "eyeball" judgments, and that's going to introduce strong biases. It's a lot more old-school than baseball. Not only is it difficult in football to get a useful quantitative value,

Football is fundamentally different from baseball and basketball. It has a lot more strategy, deception, teamwork, and on-the-fly communication between players. Something that happens innocently on one side of the field often has tremendous consequences on the other side. All this is very hard to quantify in a statistical model. For example, if your star receiver is shut down for a game, that might be because he's drawing double or triple coverage. Sure, his stats are low, but your slot and split ends can now have a field day.

The San Francisco 49ers tried a sabermetrics in their crappy years this past decade. Pioneered by the head of player personnel Paraag Marathe, they fielded a bunch of.500 and sub.500 teams before they moved him more to the business end of things and went with more traditional executives at talent evaluation.

Baseball. Can you think of another sport where the defense is the team with the ball?

Hmmm..."with the ball" is relative. Only the offensive team can make points in baseball, which makes it different than most other sports that use a ball (volleyball is the only other one I can think of with this kind of point-scoring asymmetry, if you allow an equivalence between offense and "has the serve.") In baseball, the ball switches from the defensive team to the offensive team with every pitch. There are restrictions on how the ball can be handled by the defense and the offense, certainly, and the

The reason this works particularly well in baseball, basketball and hockey is the schedule. You have 162 games a year in MLB, for example. In the NBA and NHL, it's 82 games. That's a relatively substantial sample - each game only accounts for roughly 0.6 or 1.2% of the season record.

The NFL, on the other hand, has a 16 game season. A team having a particularly good or bad game carries 10 times the weight it does in baseball (just going off the percentage of the season's games). Also, unlike baseball, football's playoffs are single-elimination.

The reason analytics aren't as directly relevant to football is exactly the reason that I enjoy it immensely.

---

Legend for our friends abroad:MLB = Major League BaseballNHL = National Hockey LeagueNBA = National Basketball AssociationNFL = National Football** League

** - yes, we're talking about American football, rather than the game known internationally as the game in which you kick a ball with your foot.

I see where you are coming from there, but it slightly misrepresents the actual analysis being done.

In baseball, the logic is "runs scored" win games (or more precisely, the run differential) and each play influences the expected runs scored over 9 innings. Good batting or good pitching is converted into equivalent run differential gained (through positive contribution on the batting side or limiting the opposition runs on the pitching side).

Because it makes too much sense to, I dunno, hire a GM and coach who know what they're doing, and bring in players that can actually perform? Is their analytical system going to take the field in place of Ryan Fitzpatrick? Couldn't get much worse, could it?

Unfortunately it's poor ownership and overall lack of leadership that is forcing you to suffer season after season after season of terrible records.

This team is hopelessly lost. They have not made the playoffs since 1998 and haven't had a winning record since 2003.

Invest in proper coaches and support staff. Commit to building a franchise instead of quick picks that you think will instantly win you a super bowl. Teams don't win with one or two guys. It takes a good (not great) quarterback, a good running back (not great) and a couple of good receivers. Couple that with a consistent defense and you can win Championships.

Look at Pittsburgh or New England. Year after Year these teams are in the hunt and have won a truck load of trophies.

New England is a proving ground for Belicheck's (sp?) prowess as a talent scout. I despise the man and his tactics, but he's been the top of heap for figuring out how to make effective use of other teams cast-off players. Eventually teams price themselves out of the competition.

I don't know that you can build a franchise anymore. You have to have that 1 or 2 super stars to build it around and they don't come around that often - but if you guess wrong on them, and the Bills do this quite often, you're

They could go the "Voodoo Witch doctor throwing darts at names in a phone book whilst simultaneously factoring in the price of tea in China" route and have equal success. That franchise is all but cursed.

I'm not sure that the analytics will work as well. football players have exceptionally short careers.

As much as I like how the game of Football plays, I will forever see it as one of the brain injury sports.

The Boston University School of Medicine studied 35 brains of former pro Football players. They found evidence of chronic traumatic encephalopathy (CTE) in 34 of them. The disease can lead to sufferers experiencing memory loss, dementia and depression.

It's fun to watch and play, but I can't support a sport that knowingly puts hundreds of thousands of kids through that. I don't know how much of this they

To make it safe, they would have to turn it into what we currently call “flag” or “touch” Football. It would be a different sport.

Or they could go the rugby route and just get rid of all that padding. There would be a modest increase in severe, acute injuries, and a drastic decrease in these long term ones. This would largely be based on the players learning not to use their heads as weapons.

The University of Texas can get just about any 5-star, Blue Chip football recruit they want. They then proceed to loose about 80% of their games againt Kansas State University, which features a roster of 2 to 3 star players and a bunch of walk-ons from tiny little Kansas towns. Why? It's akin to genius sometimes being not too far from madness; sheer athletic ability is frequently accompanied by arrogance and selfishness. It also can't help being told you're god-like in your abilities by adults since the

As far as the in game stuff goes, my guess is you could create a supervised but automated system to review game film, and more easily radio feeds to get a ton of useful data. Eventually you can throw all the 32 teams, 25